Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas but are not limited to:

Machine Learning Foundations

Machine Learning System Design
Machine Learning Optimization
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Statistical Learning
Transfer learning
Extreme Learning Machines
Kernel Based Learning
Bayesian Learning
Instruction Based Learning
Adversarial Machine Learning
	
Deep Learning and Data Engineering

Deep Neural Networks Optimization Algorithms
Deep Feedforward Networks
Regularization
Deep Convolutional Neural Networks
Deep Recurrent Neural Networks
Sequence Modelling
Deep Generative Models
Generative Adversarial Networks
Inference Dependencies on Multi-Layered Networks
Tensors for Deep Learning
Multi Scale Deep Architecture and Learning
 
Machine Learning and Data Engineering

Machine Learning in Data Lakes
Machine Learning based Data Integration and Data Interoperability
Machine Learning Data Pipelines
Machine Learning based Data Streaming
Machine Learning Relating to Knowledge and Data Management
Machine Learning Principles of Information Extraction from Big Data
Machine Learning based Web Data Management and Deep Web
Machine Learning Architecture for Pattern Recognition
Machine Learning Architecture for Medical Imaging
Machine Learning Search Engine
Machine Learning Cloud Services
Machine Learning IoT Services
	 
Applications

Bioinformatics
Biomedical informatics
Computational Biology
Healthcare
Human Activity Recognition
Computer vision
Natural Language Processing